Abstract

Exploring the conformational space of proteins is critical to characterizing their
functions. Numerous methods have been proposed to sample a protein’s conformational space,
including techniques developed in the field of robotics and known as sampling-based
motion-planning algorithms (or sampling-based planners). However, these algorithms suffer
from the curse of dimensionality when applied to large pro- teins. Many sampling-based
planners attempt to mitigate this issue by keeping track of sampling density to guide
conformational sampling toward unexplored regions of the conformational space. This is often
done using low-dimensional projections as an indirect way to reduce the dimensionality of
the exploration problem. However, how to choose an appropriate projection and how much it
influences the planner’s performance are still poorly understood problems. In this paper, we
introduce two methodologies defining low-dimensional projections that can be used by
sampling-based planners for protein conformational sampling. The first method leverages
information about a protein’s flexibility to construct projections that can efficiently
guide conformational sampling, when expert knowledge is available. The second method builds
similar projections automatically, without expert intervention. We evaluate the projections
produced by both methodologies on two conformational-search problems involving three
middle-size proteins. Our experiments demonstrate that (i) defining projections based on
expert knowledge can benefit conformational sampling, and (ii) automatically constructing
such projections is a reasonable alternative.